Overview

Dataset statistics

Number of variables19
Number of observations90944
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.3 MiB
Average record size in memory568.3 B

Variable types

Categorical6
Numeric13

Alerts

province has a high cardinality: 79 distinct values High cardinality
city has a high cardinality: 1346 distinct values High cardinality
barangay has a high cardinality: 23700 distinct values High cardinality
CLUSTERED_PRECINCTS has a high cardinality: 41669 distinct values High cardinality
POLLINGCENTER has a high cardinality: 34412 distinct values High cardinality
DOMAGOSO, ISKO MORENO (AKSYON) is highly correlated with LACSON, PING (PDR)High correlation
LACSON, PING (PDR) is highly correlated with DOMAGOSO, ISKO MORENO (AKSYON)High correlation
MARCOS, BONGBONG (PFP) is highly correlated with CLUSTERTOTALHigh correlation
CLUSTERTOTAL is highly correlated with MARCOS, BONGBONG (PFP)High correlation
MARCOS, BONGBONG (PFP) is highly correlated with CLUSTERTOTALHigh correlation
CLUSTERTOTAL is highly correlated with MARCOS, BONGBONG (PFP)High correlation
region is highly correlated with provinceHigh correlation
province is highly correlated with regionHigh correlation
region is highly correlated with province and 4 other fieldsHigh correlation
province is highly correlated with region and 7 other fieldsHigh correlation
precinct_id is highly correlated with region and 2 other fieldsHigh correlation
MANGONDATO, FAISAL (KTPNAN) is highly correlated with provinceHigh correlation
MARCOS, BONGBONG (PFP) is highly correlated with region and 3 other fieldsHigh correlation
PACQUIAO, MANNY PACMAN(PROMDI) is highly correlated with provinceHigh correlation
ROBREDO, LENI (IND) is highly correlated with region and 2 other fieldsHigh correlation
CLUSTER is highly correlated with provinceHigh correlation
CLUSTERTOTAL is highly correlated with region and 3 other fieldsHigh correlation
GONZALES, NORBERTO (PDSP) is highly skewed (γ1 = 40.47747801) Skewed
precinct_id has unique values Unique
ABELLA, ERNIE (IND) has 37891 (41.7%) zeros Zeros
DE GUZMAN, LEODY (PLM) has 43523 (47.9%) zeros Zeros
DOMAGOSO, ISKO MORENO (AKSYON) has 4054 (4.5%) zeros Zeros
GONZALES, NORBERTO (PDSP) has 42788 (47.0%) zeros Zeros
LACSON, PING (PDR) has 8720 (9.6%) zeros Zeros
MANGONDATO, FAISAL (KTPNAN) has 62183 (68.4%) zeros Zeros
MONTEMAYOR, JOSE JR. (DPP) has 56513 (62.1%) zeros Zeros
PACQUIAO, MANNY PACMAN(PROMDI) has 3903 (4.3%) zeros Zeros

Reproduction

Analysis started2022-05-21 16:38:24.246308
Analysis finished2022-05-21 16:39:37.299142
Duration1 minute and 13.05 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

region
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
REGION IV-A
13190 
REGION III
10817 
REGION VI
8395 
REGION VII
7955 
REGION V
6593 
Other values (11)
43994 

Length

Max length11
Median length10
Mean length9.386402621
Min length3

Characters and Unicode

Total characters853637
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREGION X
2nd rowREGION X
3rd rowREGION X
4th rowREGION X
5th rowREGION X

Common Values

ValueCountFrequency (%)
REGION IV-A13190
14.5%
REGION III10817
11.9%
REGION VI8395
9.2%
REGION VII7955
8.7%
REGION V6593
 
7.2%
REGION I5908
 
6.5%
REGION VIII5748
 
6.3%
REGION X4925
 
5.4%
REGION XI4653
 
5.1%
REGION XII4037
 
4.4%
Other values (6)18723
20.6%

Length

2022-05-22T00:39:37.392565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
region86171
48.7%
iv-a13190
 
7.4%
iii10817
 
6.1%
vi8395
 
4.7%
vii7955
 
4.5%
v6593
 
3.7%
i5908
 
3.3%
viii5748
 
3.2%
x4925
 
2.8%
xi4653
 
2.6%
Other values (7)22760
 
12.9%

Most occurring characters

ValueCountFrequency (%)
I215853
25.3%
R90152
10.6%
O86963
10.2%
E86171
 
10.1%
G86171
 
10.1%
N86171
 
10.1%
86171
 
10.1%
V45962
 
5.4%
X20349
 
2.4%
A17963
 
2.1%
Other values (3)31711
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter750987
88.0%
Space Separator86171
 
10.1%
Dash Punctuation16479
 
1.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I215853
28.7%
R90152
12.0%
O86963
11.6%
E86171
 
11.5%
G86171
 
11.5%
N86171
 
11.5%
V45962
 
6.1%
X20349
 
2.7%
A17963
 
2.4%
M7962
 
1.1%
Space Separator
ValueCountFrequency (%)
86171
100.0%
Dash Punctuation
ValueCountFrequency (%)
-16479
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin750987
88.0%
Common102650
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I215853
28.7%
R90152
12.0%
O86963
11.6%
E86171
 
11.5%
G86171
 
11.5%
N86171
 
11.5%
V45962
 
6.1%
X20349
 
2.7%
A17963
 
2.4%
M7962
 
1.1%
Common
ValueCountFrequency (%)
86171
83.9%
-16479
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII853637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I215853
25.3%
R90152
10.6%
O86963
10.2%
E86171
 
10.1%
G86171
 
10.1%
N86171
 
10.1%
86171
 
10.1%
V45962
 
5.4%
X20349
 
2.4%
A17963
 
2.1%
Other values (3)31711
 
3.7%

province
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct79
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
CEBU
 
4650
PANGASINAN
 
3307
CAVITE
 
3099
ILOILO
 
3019
LAGUNA
 
2918
Other values (74)
73951 

Length

Max length23
Median length17
Mean length9.639525422
Min length4

Characters and Unicode

Total characters876657
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBUKIDNON
2nd rowBUKIDNON
3rd rowBUKIDNON
4th rowBUKIDNON
5th rowBUKIDNON

Common Values

ValueCountFrequency (%)
CEBU4650
 
5.1%
PANGASINAN3307
 
3.6%
CAVITE3099
 
3.4%
ILOILO3019
 
3.3%
LAGUNA2918
 
3.2%
BULACAN2833
 
3.1%
NEGROS OCCIDENTAL2823
 
3.1%
BATANGAS2668
 
2.9%
NUEVA ECIJA2367
 
2.6%
RIZAL2273
 
2.5%
Other values (69)60987
67.1%

Length

2022-05-22T00:39:37.511837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
del10879
 
7.9%
sur9515
 
6.9%
norte5825
 
4.2%
davao4653
 
3.4%
cebu4650
 
3.4%
oriental4552
 
3.3%
occidental4405
 
3.2%
negros4244
 
3.1%
zamboanga3744
 
2.7%
pangasinan3307
 
2.4%
Other values (71)82321
59.6%

Most occurring characters

ValueCountFrequency (%)
A148540
16.9%
N77461
 
8.8%
O66549
 
7.6%
E63339
 
7.2%
L53653
 
6.1%
I52223
 
6.0%
S48113
 
5.5%
47151
 
5.4%
R44576
 
5.1%
U40139
 
4.6%
Other values (17)234913
26.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter829106
94.6%
Space Separator47151
 
5.4%
Dash Punctuation400
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A148540
17.9%
N77461
 
9.3%
O66549
 
8.0%
E63339
 
7.6%
L53653
 
6.5%
I52223
 
6.3%
S48113
 
5.8%
R44576
 
5.4%
U40139
 
4.8%
T39676
 
4.8%
Other values (15)194837
23.5%
Space Separator
ValueCountFrequency (%)
47151
100.0%
Dash Punctuation
ValueCountFrequency (%)
-400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin829106
94.6%
Common47551
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A148540
17.9%
N77461
 
9.3%
O66549
 
8.0%
E63339
 
7.6%
L53653
 
6.5%
I52223
 
6.3%
S48113
 
5.8%
R44576
 
5.4%
U40139
 
4.8%
T39676
 
4.8%
Other values (15)194837
23.5%
Common
ValueCountFrequency (%)
47151
99.2%
-400
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII876657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A148540
16.9%
N77461
 
8.8%
O66549
 
7.6%
E63339
 
7.2%
L53653
 
6.1%
I52223
 
6.0%
S48113
 
5.5%
47151
 
5.4%
R44576
 
5.1%
U40139
 
4.6%
Other values (17)234913
26.8%

city
Categorical

HIGH CARDINALITY

Distinct1346
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
CITY OF DAVAO
 
1266
CITY OF CEBU
 
888
CITY OF ANTIPOLO
 
650
CITY OF ZAMBOANGA
 
625
CITY OF DASMARIÑAS
 
527
Other values (1341)
86988 

Length

Max length31
Median length24
Mean length10.04997581
Min length3

Characters and Unicode

Total characters913985
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBAUNGON
2nd rowBAUNGON
3rd rowBAUNGON
4th rowBAUNGON
5th rowBAUNGON

Common Values

ValueCountFrequency (%)
CITY OF DAVAO1266
 
1.4%
CITY OF CEBU888
 
1.0%
CITY OF ANTIPOLO650
 
0.7%
CITY OF ZAMBOANGA625
 
0.7%
CITY OF DASMARIÑAS527
 
0.6%
CITY OF CAGAYAN DE ORO523
 
0.6%
CITY OF ILOILO499
 
0.5%
CITY OF GENERAL SANTOS496
 
0.5%
CITY OF CALAMBA468
 
0.5%
SANTA CRUZ454
 
0.5%
Other values (1336)84548
93.0%

Length

2022-05-22T00:39:37.642245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city24623
 
15.5%
of24189
 
15.2%
san6024
 
3.8%
santa2119
 
1.3%
davao1266
 
0.8%
jose1102
 
0.7%
general1017
 
0.6%
cebu888
 
0.6%
antipolo650
 
0.4%
fernando628
 
0.4%
Other values (1384)96671
60.7%

Most occurring characters

ValueCountFrequency (%)
A155380
17.0%
O78467
 
8.6%
N70088
 
7.7%
I70029
 
7.7%
68233
 
7.5%
T53637
 
5.9%
C47319
 
5.2%
L42743
 
4.7%
S36495
 
4.0%
Y35550
 
3.9%
Other values (21)256044
28.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter843657
92.3%
Space Separator68233
 
7.5%
Other Punctuation1060
 
0.1%
Dash Punctuation1035
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A155380
18.4%
O78467
 
9.3%
N70088
 
8.3%
I70029
 
8.3%
T53637
 
6.4%
C47319
 
5.6%
L42743
 
5.1%
S36495
 
4.3%
Y35550
 
4.2%
G30471
 
3.6%
Other values (17)223478
26.5%
Other Punctuation
ValueCountFrequency (%)
.789
74.4%
'271
 
25.6%
Space Separator
ValueCountFrequency (%)
68233
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1035
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin843657
92.3%
Common70328
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A155380
18.4%
O78467
 
9.3%
N70088
 
8.3%
I70029
 
8.3%
T53637
 
6.4%
C47319
 
5.6%
L42743
 
5.1%
S36495
 
4.3%
Y35550
 
4.2%
G30471
 
3.6%
Other values (17)223478
26.5%
Common
ValueCountFrequency (%)
68233
97.0%
-1035
 
1.5%
.789
 
1.1%
'271
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII912521
99.8%
None1464
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A155380
17.0%
O78467
 
8.6%
N70088
 
7.7%
I70029
 
7.7%
68233
 
7.5%
T53637
 
5.9%
C47319
 
5.2%
L42743
 
4.7%
S36495
 
4.0%
Y35550
 
3.9%
Other values (20)254580
27.9%
None
ValueCountFrequency (%)
Ñ1464
100.0%

barangay
Categorical

HIGH CARDINALITY

Distinct23700
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
POBLACION
 
2658
SAN ISIDRO
 
900
SAN JOSE
 
664
SAN VICENTE
 
494
SAN ROQUE
 
477
Other values (23695)
85751 

Length

Max length44
Median length38
Mean length9.233165464
Min length2

Characters and Unicode

Total characters839701
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8633 ?
Unique (%)9.5%

Sample

1st rowBALINTAD
2nd rowBUENAVISTA
3rd rowDANATAG
4th rowDANATAG
5th rowDANATAG

Common Values

ValueCountFrequency (%)
POBLACION2658
 
2.9%
SAN ISIDRO900
 
1.0%
SAN JOSE664
 
0.7%
SAN VICENTE494
 
0.5%
SAN ROQUE477
 
0.5%
SAN JUAN462
 
0.5%
SAN ANTONIO401
 
0.4%
SANTO NIÑO384
 
0.4%
SANTA CRUZ366
 
0.4%
SAN MIGUEL300
 
0.3%
Other values (23690)83838
92.2%

Length

2022-05-22T00:39:37.769130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san7970
 
6.1%
pob6800
 
5.2%
poblacion4135
 
3.2%
santa1810
 
1.4%
barangay1567
 
1.2%
ii1152
 
0.9%
i1139
 
0.9%
santo1089
 
0.8%
isidro1083
 
0.8%
jose994
 
0.8%
Other values (19602)102351
78.7%

Most occurring characters

ValueCountFrequency (%)
A156789
18.7%
N85086
 
10.1%
O64845
 
7.7%
I55384
 
6.6%
L42366
 
5.0%
B41074
 
4.9%
39146
 
4.7%
S37830
 
4.5%
G37031
 
4.4%
T31657
 
3.8%
Other values (35)248493
29.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter773171
92.1%
Space Separator39146
 
4.7%
Other Punctuation7825
 
0.9%
Close Punctuation6380
 
0.8%
Open Punctuation6380
 
0.8%
Dash Punctuation4082
 
0.5%
Decimal Number2717
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A156789
20.3%
N85086
11.0%
O64845
 
8.4%
I55384
 
7.2%
L42366
 
5.5%
B41074
 
5.3%
S37830
 
4.9%
G37031
 
4.8%
T31657
 
4.1%
U30086
 
3.9%
Other values (17)191023
24.7%
Decimal Number
ValueCountFrequency (%)
1746
27.5%
2543
20.0%
3352
13.0%
4220
 
8.1%
5187
 
6.9%
6166
 
6.1%
0159
 
5.9%
9122
 
4.5%
8118
 
4.3%
7104
 
3.8%
Other Punctuation
ValueCountFrequency (%)
.7771
99.3%
'44
 
0.6%
/8
 
0.1%
*2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
39146
100.0%
Close Punctuation
ValueCountFrequency (%)
)6380
100.0%
Open Punctuation
ValueCountFrequency (%)
(6380
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4082
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin773171
92.1%
Common66530
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A156789
20.3%
N85086
11.0%
O64845
 
8.4%
I55384
 
7.2%
L42366
 
5.5%
B41074
 
5.3%
S37830
 
4.9%
G37031
 
4.8%
T31657
 
4.1%
U30086
 
3.9%
Other values (17)191023
24.7%
Common
ValueCountFrequency (%)
39146
58.8%
.7771
 
11.7%
)6380
 
9.6%
(6380
 
9.6%
-4082
 
6.1%
1746
 
1.1%
2543
 
0.8%
3352
 
0.5%
4220
 
0.3%
5187
 
0.3%
Other values (8)723
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII838730
99.9%
None971
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A156789
18.7%
N85086
 
10.1%
O64845
 
7.7%
I55384
 
6.6%
L42366
 
5.1%
B41074
 
4.9%
39146
 
4.7%
S37830
 
4.5%
G37031
 
4.4%
T31657
 
3.8%
Other values (34)247522
29.5%
None
ValueCountFrequency (%)
Ñ971
100.0%

precinct_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct90944
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39156810.06
Minimum2010001
Maximum93150021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:37.894378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2010001
5-th percentile7020080.15
Q121200212.75
median37260055.5
Q355360045.25
95-th percentile73320265.85
Maximum93150021
Range91140020
Interquartile range (IQR)34159832.5

Descriptive statistics

Standard deviation21501228.82
Coefficient of variation (CV)0.5491057313
Kurtosis-0.869922715
Mean39156810.06
Median Absolute Deviation (MAD)16910002
Skewness0.2346655242
Sum3.561076934 × 1012
Variance4.623028409 × 1014
MonotonicityNot monotonic
2022-05-22T00:39:38.026554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130100091
 
< 0.1%
471000661
 
< 0.1%
471000421
 
< 0.1%
471000411
 
< 0.1%
471000401
 
< 0.1%
471000391
 
< 0.1%
471000381
 
< 0.1%
471000371
 
< 0.1%
471000361
 
< 0.1%
471000351
 
< 0.1%
Other values (90934)90934
> 99.9%
ValueCountFrequency (%)
20100011
< 0.1%
20100021
< 0.1%
20100031
< 0.1%
20100041
< 0.1%
20100051
< 0.1%
20100061
< 0.1%
20100071
< 0.1%
20100081
< 0.1%
20100091
< 0.1%
20100101
< 0.1%
ValueCountFrequency (%)
931500211
< 0.1%
931500201
< 0.1%
931500191
< 0.1%
931500181
< 0.1%
931500171
< 0.1%
931500161
< 0.1%
931500151
< 0.1%
931500141
< 0.1%
931500101
< 0.1%
931500081
< 0.1%

ABELLA, ERNIE (IND)
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.167223786
Minimum0
Maximum33
Zeros37891
Zeros (%)41.7%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:38.142070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.51002085
Coefficient of variation (CV)1.29368581
Kurtosis16.96523782
Mean1.167223786
Median Absolute Deviation (MAD)1
Skewness2.68437044
Sum106152
Variance2.280162966
MonotonicityNot monotonic
2022-05-22T00:39:38.241167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
037891
41.7%
126402
29.0%
213788
 
15.2%
36559
 
7.2%
43146
 
3.5%
51508
 
1.7%
6748
 
0.8%
7361
 
0.4%
8208
 
0.2%
9133
 
0.1%
Other values (18)200
 
0.2%
ValueCountFrequency (%)
037891
41.7%
126402
29.0%
213788
 
15.2%
36559
 
7.2%
43146
 
3.5%
51508
 
1.7%
6748
 
0.8%
7361
 
0.4%
8208
 
0.2%
9133
 
0.1%
ValueCountFrequency (%)
331
 
< 0.1%
291
 
< 0.1%
261
 
< 0.1%
252
< 0.1%
233
< 0.1%
221
 
< 0.1%
211
 
< 0.1%
201
 
< 0.1%
192
< 0.1%
184
< 0.1%

DE GUZMAN, LEODY (PLM)
Real number (ℝ≥0)

ZEROS

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8837966221
Minimum0
Maximum66
Zeros43523
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:38.351254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum66
Range66
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.233777049
Coefficient of variation (CV)1.395996566
Kurtosis213.6904377
Mean0.8837966221
Median Absolute Deviation (MAD)1
Skewness6.783433344
Sum80376
Variance1.522205807
MonotonicityNot monotonic
2022-05-22T00:39:38.457834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
043523
47.9%
127504
30.2%
212320
 
13.5%
34739
 
5.2%
41752
 
1.9%
5619
 
0.7%
6239
 
0.3%
7116
 
0.1%
846
 
0.1%
920
 
< 0.1%
Other values (21)66
 
0.1%
ValueCountFrequency (%)
043523
47.9%
127504
30.2%
212320
 
13.5%
34739
 
5.2%
41752
 
1.9%
5619
 
0.7%
6239
 
0.3%
7116
 
0.1%
846
 
0.1%
920
 
< 0.1%
ValueCountFrequency (%)
661
< 0.1%
581
< 0.1%
511
< 0.1%
411
< 0.1%
342
< 0.1%
321
< 0.1%
301
< 0.1%
272
< 0.1%
261
< 0.1%
241
< 0.1%

DOMAGOSO, ISKO MORENO (AKSYON)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct161
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.59366203
Minimum0
Maximum351
Zeros4054
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:38.585143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q321
95-th percentile39
Maximum351
Range351
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.36843048
Coefficient of variation (CV)0.9160435842
Kurtosis20.90718928
Mean14.59366203
Median Absolute Deviation (MAD)7
Skewness2.515340264
Sum1327206
Variance178.7149334
MonotonicityNot monotonic
2022-05-22T00:39:38.710668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34447
 
4.9%
24406
 
4.8%
44344
 
4.8%
54314
 
4.7%
14059
 
4.5%
04054
 
4.5%
63962
 
4.4%
73861
 
4.2%
83590
 
3.9%
93458
 
3.8%
Other values (151)50449
55.5%
ValueCountFrequency (%)
04054
4.5%
14059
4.5%
24406
4.8%
34447
4.9%
44344
4.8%
54314
4.7%
63962
4.4%
73861
4.2%
83590
3.9%
93458
3.8%
ValueCountFrequency (%)
3511
< 0.1%
3391
< 0.1%
2561
< 0.1%
2401
< 0.1%
2231
< 0.1%
2131
< 0.1%
2001
< 0.1%
1881
< 0.1%
1841
< 0.1%
1801
< 0.1%

GONZALES, NORBERTO (PDSP)
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8981131246
Minimum0
Maximum197
Zeros42788
Zeros (%)47.0%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:38.828976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum197
Range197
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.414158809
Coefficient of variation (CV)1.574588735
Kurtosis4961.553315
Mean0.8981131246
Median Absolute Deviation (MAD)1
Skewness40.47747801
Sum81678
Variance1.999845136
MonotonicityNot monotonic
2022-05-22T00:39:38.926858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
042788
47.0%
128035
30.8%
212369
 
13.6%
34827
 
5.3%
41725
 
1.9%
5685
 
0.8%
6262
 
0.3%
7119
 
0.1%
856
 
0.1%
922
 
< 0.1%
Other values (12)56
 
0.1%
ValueCountFrequency (%)
042788
47.0%
128035
30.8%
212369
 
13.6%
34827
 
5.3%
41725
 
1.9%
5685
 
0.8%
6262
 
0.3%
7119
 
0.1%
856
 
0.1%
922
 
< 0.1%
ValueCountFrequency (%)
1971
 
< 0.1%
1341
 
< 0.1%
571
 
< 0.1%
281
 
< 0.1%
232
 
< 0.1%
182
 
< 0.1%
152
 
< 0.1%
143
 
< 0.1%
139
< 0.1%
128
< 0.1%

LACSON, PING (PDR)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct125
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.682694845
Minimum0
Maximum225
Zeros8720
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:39.049344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q311
95-th percentile22
Maximum225
Range225
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.510086165
Coefficient of variation (CV)1.107695455
Kurtosis44.90629769
Mean7.682694845
Median Absolute Deviation (MAD)4
Skewness4.078346402
Sum698695
Variance72.42156653
MonotonicityNot monotonic
2022-05-22T00:39:39.173476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08720
 
9.6%
18514
 
9.4%
28282
 
9.1%
37606
 
8.4%
46989
 
7.7%
56150
 
6.8%
65460
 
6.0%
74818
 
5.3%
84289
 
4.7%
93789
 
4.2%
Other values (115)26327
28.9%
ValueCountFrequency (%)
08720
9.6%
18514
9.4%
28282
9.1%
37606
8.4%
46989
7.7%
56150
6.8%
65460
6.0%
74818
5.3%
84289
4.7%
93789
4.2%
ValueCountFrequency (%)
2251
< 0.1%
2181
< 0.1%
2151
< 0.1%
2121
< 0.1%
2021
< 0.1%
1931
< 0.1%
1661
< 0.1%
1641
< 0.1%
1601
< 0.1%
1431
< 0.1%

MANGONDATO, FAISAL (KTPNAN)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct329
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.657932354
Minimum0
Maximum573
Zeros62183
Zeros (%)68.4%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:39.301221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum573
Range573
Interquartile range (IQR)1

Descriptive statistics

Standard deviation18.55126433
Coefficient of variation (CV)6.979584826
Kurtosis190.7911303
Mean2.657932354
Median Absolute Deviation (MAD)0
Skewness12.46620046
Sum241723
Variance344.1494081
MonotonicityNot monotonic
2022-05-22T00:39:39.427922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062183
68.4%
116899
 
18.6%
24500
 
4.9%
31597
 
1.8%
4850
 
0.9%
5639
 
0.7%
6484
 
0.5%
7345
 
0.4%
8306
 
0.3%
9262
 
0.3%
Other values (319)2879
 
3.2%
ValueCountFrequency (%)
062183
68.4%
116899
 
18.6%
24500
 
4.9%
31597
 
1.8%
4850
 
0.9%
5639
 
0.7%
6484
 
0.5%
7345
 
0.4%
8306
 
0.3%
9262
 
0.3%
ValueCountFrequency (%)
5731
< 0.1%
5631
< 0.1%
5291
< 0.1%
4951
< 0.1%
4611
< 0.1%
4531
< 0.1%
4511
< 0.1%
4431
< 0.1%
4411
< 0.1%
4361
< 0.1%

MARCOS, BONGBONG (PFP)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct839
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290.289002
Minimum0
Maximum1105
Zeros352
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:39.561115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1182
median300
Q3393
95-th percentile515
Maximum1105
Range1105
Interquartile range (IQR)211

Descriptive statistics

Standard deviation143.0980682
Coefficient of variation (CV)0.4929503607
Kurtosis-0.3952529356
Mean290.289002
Median Absolute Deviation (MAD)104
Skewness0.03593745601
Sum26400043
Variance20477.05714
MonotonicityNot monotonic
2022-05-22T00:39:39.682687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0352
 
0.4%
345290
 
0.3%
378272
 
0.3%
365269
 
0.3%
356269
 
0.3%
383269
 
0.3%
354268
 
0.3%
343268
 
0.3%
349267
 
0.3%
364266
 
0.3%
Other values (829)88154
96.9%
ValueCountFrequency (%)
0352
0.4%
141
 
< 0.1%
224
 
< 0.1%
333
 
< 0.1%
427
 
< 0.1%
542
 
< 0.1%
637
 
< 0.1%
748
 
0.1%
842
 
< 0.1%
947
 
0.1%
ValueCountFrequency (%)
11051
< 0.1%
10801
< 0.1%
10391
< 0.1%
10261
< 0.1%
10131
< 0.1%
10121
< 0.1%
9641
< 0.1%
9621
< 0.1%
9401
< 0.1%
9161
< 0.1%

MONTEMAYOR, JOSE JR. (DPP)
Real number (ℝ≥0)

ZEROS

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5841726777
Minimum0
Maximum34
Zeros56513
Zeros (%)62.1%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:39.800912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum34
Range34
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.025089405
Coefficient of variation (CV)1.754771225
Kurtosis70.37810214
Mean0.5841726777
Median Absolute Deviation (MAD)0
Skewness4.881320632
Sum53127
Variance1.050808288
MonotonicityNot monotonic
2022-05-22T00:39:39.906171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
056513
62.1%
123026
25.3%
27508
 
8.3%
32398
 
2.6%
4821
 
0.9%
5322
 
0.4%
6133
 
0.1%
779
 
0.1%
838
 
< 0.1%
928
 
< 0.1%
Other values (17)78
 
0.1%
ValueCountFrequency (%)
056513
62.1%
123026
25.3%
27508
 
8.3%
32398
 
2.6%
4821
 
0.9%
5322
 
0.4%
6133
 
0.1%
779
 
0.1%
838
 
< 0.1%
928
 
< 0.1%
ValueCountFrequency (%)
341
 
< 0.1%
321
 
< 0.1%
301
 
< 0.1%
291
 
< 0.1%
241
 
< 0.1%
232
 
< 0.1%
202
 
< 0.1%
191
 
< 0.1%
185
< 0.1%
173
< 0.1%

PACQUIAO, MANNY PACMAN(PROMDI)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct457
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.14518825
Minimum0
Maximum615
Zeros3903
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:40.034559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median16
Q348
95-th percentile150
Maximum615
Range615
Interquartile range (IQR)42

Descriptive statistics

Standard deviation53.91281206
Coefficient of variation (CV)1.413358133
Kurtosis10.58535906
Mean38.14518825
Median Absolute Deviation (MAD)13
Skewness2.828618299
Sum3469076
Variance2906.591304
MonotonicityNot monotonic
2022-05-22T00:39:40.179471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03903
 
4.3%
33257
 
3.6%
23194
 
3.5%
53158
 
3.5%
43110
 
3.4%
13086
 
3.4%
63080
 
3.4%
72999
 
3.3%
82840
 
3.1%
92716
 
3.0%
Other values (447)59601
65.5%
ValueCountFrequency (%)
03903
4.3%
13086
3.4%
23194
3.5%
33257
3.6%
43110
3.4%
53158
3.5%
63080
3.4%
72999
3.3%
82840
3.1%
92716
3.0%
ValueCountFrequency (%)
6151
< 0.1%
6141
< 0.1%
5641
< 0.1%
5541
< 0.1%
5371
< 0.1%
5331
< 0.1%
5301
< 0.1%
5161
< 0.1%
5101
< 0.1%
5081
< 0.1%

ROBREDO, LENI (IND)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct667
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.7177054
Minimum0
Maximum790
Zeros816
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:40.726235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q149
median114
Q3195
95-th percentile369
Maximum790
Range790
Interquartile range (IQR)146

Descriptive statistics

Standard deviation114.0588319
Coefficient of variation (CV)0.8222370139
Kurtosis1.834069528
Mean138.7177054
Median Absolute Deviation (MAD)70
Skewness1.300108528
Sum12615543
Variance13009.41713
MonotonicityNot monotonic
2022-05-22T00:39:40.857183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0816
 
0.9%
25593
 
0.7%
26572
 
0.6%
40545
 
0.6%
38540
 
0.6%
36539
 
0.6%
23539
 
0.6%
33532
 
0.6%
24531
 
0.6%
29523
 
0.6%
Other values (657)85214
93.7%
ValueCountFrequency (%)
0816
0.9%
1260
 
0.3%
2277
 
0.3%
3274
 
0.3%
4307
 
0.3%
5306
 
0.3%
6340
0.4%
7303
 
0.3%
8345
0.4%
9335
0.4%
ValueCountFrequency (%)
7901
< 0.1%
7441
< 0.1%
7431
< 0.1%
7351
< 0.1%
7321
< 0.1%
7281
< 0.1%
7271
< 0.1%
7182
< 0.1%
6991
< 0.1%
6981
< 0.1%

CLUSTER
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1376
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.05503387
Minimum1
Maximum1411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:40.992372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median35
Q372
95-th percentile272
Maximum1411
Range1410
Interquartile range (IQR)56

Descriptive statistics

Standard deviation129.8092569
Coefficient of variation (CV)1.776869437
Kurtosis34.22968045
Mean73.05503387
Median Absolute Deviation (MAD)23
Skewness5.097053437
Sum6643917
Variance16850.44317
MonotonicityNot monotonic
2022-05-22T00:39:41.118853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121472
 
1.6%
91469
 
1.6%
111467
 
1.6%
101464
 
1.6%
131459
 
1.6%
81456
 
1.6%
71453
 
1.6%
61452
 
1.6%
141451
 
1.6%
51445
 
1.6%
Other values (1366)76356
84.0%
ValueCountFrequency (%)
11438
1.6%
21437
1.6%
31441
1.6%
41442
1.6%
51445
1.6%
61452
1.6%
71453
1.6%
81456
1.6%
91469
1.6%
101464
1.6%
ValueCountFrequency (%)
14111
< 0.1%
14101
< 0.1%
14091
< 0.1%
14081
< 0.1%
14071
< 0.1%
14061
< 0.1%
14051
< 0.1%
14041
< 0.1%
14031
< 0.1%
14021
< 0.1%

CLUSTERTOTAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct764
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean618.9877397
Minimum1
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size710.6 KiB
2022-05-22T00:39:41.256574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile342
Q1533
median635
Q3740
95-th percentile792
Maximum2000
Range1999
Interquartile range (IQR)207

Descriptive statistics

Standard deviation157.4532994
Coefficient of variation (CV)0.2543722425
Kurtosis8.534712386
Mean618.9877397
Median Absolute Deviation (MAD)104
Skewness0.3512319603
Sum56293221
Variance24791.54151
MonotonicityNot monotonic
2022-05-22T00:39:41.386243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000612
 
0.7%
800499
 
0.5%
790496
 
0.5%
789488
 
0.5%
788483
 
0.5%
600481
 
0.5%
795456
 
0.5%
787452
 
0.5%
793452
 
0.5%
784450
 
0.5%
Other values (754)86075
94.6%
ValueCountFrequency (%)
14
< 0.1%
21
 
< 0.1%
62
< 0.1%
171
 
< 0.1%
261
 
< 0.1%
431
 
< 0.1%
511
 
< 0.1%
531
 
< 0.1%
551
 
< 0.1%
561
 
< 0.1%
ValueCountFrequency (%)
2000133
 
0.1%
18961
 
< 0.1%
18201
 
< 0.1%
18091
 
< 0.1%
14791
 
< 0.1%
13001
 
< 0.1%
1000612
0.7%
9981
 
< 0.1%
9821
 
< 0.1%
9661
 
< 0.1%

CLUSTERED_PRECINCTS
Categorical

HIGH CARDINALITY

Distinct41669
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Memory size7.1 MiB
0001A, 0001B, 0002A, 0002B
 
185
0029A, 0029B, 0030A, 0030B
 
101
0025A, 0025B, 0026A, 0026B
 
101
0023A, 0023B, 0024A, 0024B
 
96
0015A, 0015B, 0016A, 0016B
 
94
Other values (41664)
90367 

Length

Max length103
Median length96
Mean length24.7635358
Min length4

Characters and Unicode

Total characters2252095
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32971 ?
Unique (%)36.3%

Sample

1st row0014A, 0015A, 0015B, 0014B
2nd row0016A, 0016B, 0017A, 0017B, 0017C
3rd row0018A, 0018B, 0019A, 0019B
4th row0020A, 0020B, 0021A, 0021B, 0024A
5th row0022A, 0022B, 0023A, 0023B

Common Values

ValueCountFrequency (%)
0001A, 0001B, 0002A, 0002B185
 
0.2%
0029A, 0029B, 0030A, 0030B101
 
0.1%
0025A, 0025B, 0026A, 0026B101
 
0.1%
0023A, 0023B, 0024A, 0024B96
 
0.1%
0015A, 0015B, 0016A, 0016B94
 
0.1%
0024A, 0024B, 0025A, 0025B93
 
0.1%
0034A, 0034B, 0035A, 0035B92
 
0.1%
0003A, 0003B, 0004A, 0004B92
 
0.1%
0020A, 0020B, 0021A, 0021B91
 
0.1%
0012A, 0012B, 0013A, 0013B91
 
0.1%
Other values (41659)89908
98.9%

Length

2022-05-22T00:39:41.539995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0013a1449
 
0.4%
0014a1448
 
0.4%
0016a1448
 
0.4%
0019a1444
 
0.4%
0017a1443
 
0.4%
0018a1443
 
0.4%
0015a1442
 
0.4%
0011a1441
 
0.4%
0012a1440
 
0.4%
0010a1436
 
0.4%
Other values (15036)332394
95.8%

Most occurring characters

ValueCountFrequency (%)
0628398
27.9%
,256007
11.4%
255884
11.4%
A180245
 
8.0%
1161331
 
7.2%
B114255
 
5.1%
2106918
 
4.7%
390737
 
4.0%
480448
 
3.6%
574697
 
3.3%
Other values (32)303175
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1392711
61.8%
Uppercase Letter347488
 
15.4%
Other Punctuation256007
 
11.4%
Space Separator255884
 
11.4%
Lowercase Letter5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A180245
51.9%
B114255
32.9%
C27182
 
7.8%
D8533
 
2.5%
P5531
 
1.6%
E3820
 
1.1%
F2133
 
0.6%
G1356
 
0.4%
H982
 
0.3%
I851
 
0.2%
Other values (16)2600
 
0.7%
Decimal Number
ValueCountFrequency (%)
0628398
45.1%
1161331
 
11.6%
2106918
 
7.7%
390737
 
6.5%
480448
 
5.8%
574697
 
5.4%
669657
 
5.0%
764017
 
4.6%
859842
 
4.3%
956666
 
4.1%
Lowercase Letter
ValueCountFrequency (%)
c2
40.0%
d1
20.0%
b1
20.0%
a1
20.0%
Other Punctuation
ValueCountFrequency (%)
,256007
100.0%
Space Separator
ValueCountFrequency (%)
255884
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1904602
84.6%
Latin347493
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A180245
51.9%
B114255
32.9%
C27182
 
7.8%
D8533
 
2.5%
P5531
 
1.6%
E3820
 
1.1%
F2133
 
0.6%
G1356
 
0.4%
H982
 
0.3%
I851
 
0.2%
Other values (20)2605
 
0.7%
Common
ValueCountFrequency (%)
0628398
33.0%
,256007
13.4%
255884
13.4%
1161331
 
8.5%
2106918
 
5.6%
390737
 
4.8%
480448
 
4.2%
574697
 
3.9%
669657
 
3.7%
764017
 
3.4%
Other values (2)116508
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2252095
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0628398
27.9%
,256007
11.4%
255884
11.4%
A180245
 
8.0%
1161331
 
7.2%
B114255
 
5.1%
2106918
 
4.7%
390737
 
4.0%
480448
 
3.6%
574697
 
3.3%
Other values (32)303175
13.5%

POLLINGCENTER
Categorical

HIGH CARDINALITY

Distinct34412
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
RIYADH PE, KINGDOM OF SAUDI ARABIA (RIYADH)
 
132
HONGKONG PCG, PEOPLES REPUBLIC OF CHINA
 
94
SINGAPORE PE, REPUBLIC OF SINGAPORE
 
85
JEDDAH PCG, KINGDOM OF SAUDI ARABIA (RIYADH)
 
66
SANTA CRUZ ELEMENTARY SCHOOL, OLALIA ROAD, BRGY. SANTA CRUZ
 
51
Other values (34407)
90516 

Length

Max length149
Median length107
Mean length57.99625044
Min length14

Characters and Unicode

Total characters5274411
Distinct characters55
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13548 ?
Unique (%)14.9%

Sample

1st rowBALINTAD, ELEMENTARY SCHOOL, BALINTAD, BAUNGON, BUKIDNON
2nd rowBUENAVISTA, ELEMENTARY SCHOOL, BUENAVISTA, BAUNGON, BUKIDNON
3rd rowDANATAG, ELEMENTARY SCHOOL, DANATAG, BAUNGON, BUKIDNON
4th rowDANATAG, ELEMENTARY SCHOOL, DANATAG, BAUNGON, BUKIDNON
5th rowDANATAG, ELEMENTARY SCHOOL, DANATAG, BAUNGON, BUKIDNON

Common Values

ValueCountFrequency (%)
RIYADH PE, KINGDOM OF SAUDI ARABIA (RIYADH)132
 
0.1%
HONGKONG PCG, PEOPLES REPUBLIC OF CHINA94
 
0.1%
SINGAPORE PE, REPUBLIC OF SINGAPORE85
 
0.1%
JEDDAH PCG, KINGDOM OF SAUDI ARABIA (RIYADH)66
 
0.1%
SANTA CRUZ ELEMENTARY SCHOOL, OLALIA ROAD, BRGY. SANTA CRUZ51
 
0.1%
POLO AL KHOBAR, KINGDOM OF SAUDI ARABIA (RIYADH)51
 
0.1%
MAMATID ELEMENTARY SCHOOL, BARANGAY MAMATID, CABUYAO CITY, LAGUNA46
 
0.1%
STA. CRUZ ELEMENTARY SCHOOL, STA. CRUZ I43
 
< 0.1%
SAN JOSE NATIONAL HIGH SCHOOL, SITIO PULONG BANAL, CIRCUMFERENTIAL ROAD, BRGY. SAN JOSE42
 
< 0.1%
LAHUG ELEMENTARY SCHOOL, GORORDO AVENUE, CEBU CITY42
 
< 0.1%
Other values (34402)90292
99.3%

Length

2022-05-22T00:39:41.685514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
school84753
 
12.1%
elementary64255
 
9.2%
san18421
 
2.6%
brgy17320
 
2.5%
city15982
 
2.3%
barangay13097
 
1.9%
central9883
 
1.4%
purok7220
 
1.0%
sur6820
 
1.0%
elem6375
 
0.9%
Other values (23841)454593
65.1%

Most occurring characters

ValueCountFrequency (%)
A657181
12.5%
608521
 
11.5%
O399689
 
7.6%
N367918
 
7.0%
E355286
 
6.7%
L335055
 
6.4%
R244168
 
4.6%
S232267
 
4.4%
,225888
 
4.3%
T223176
 
4.2%
Other values (45)1625262
30.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4363849
82.7%
Space Separator608521
 
11.5%
Other Punctuation275200
 
5.2%
Decimal Number10719
 
0.2%
Dash Punctuation9155
 
0.2%
Open Punctuation3487
 
0.1%
Close Punctuation3473
 
0.1%
Lowercase Letter5
 
< 0.1%
Modifier Symbol2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A657181
15.1%
O399689
 
9.2%
N367918
 
8.4%
E355286
 
8.1%
L335055
 
7.7%
R244168
 
5.6%
S232267
 
5.3%
T223176
 
5.1%
I218341
 
5.0%
C209821
 
4.8%
Other values (18)1120947
25.7%
Other Punctuation
ValueCountFrequency (%)
,225888
82.1%
.48594
 
17.7%
/415
 
0.2%
&162
 
0.1%
'81
 
< 0.1%
"30
 
< 0.1%
:15
 
< 0.1%
#9
 
< 0.1%
*4
 
< 0.1%
;2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
13387
31.6%
22318
21.6%
31483
13.8%
41027
 
9.6%
5746
 
7.0%
0429
 
4.0%
6413
 
3.9%
8400
 
3.7%
7370
 
3.5%
9146
 
1.4%
Open Punctuation
ValueCountFrequency (%)
(3482
99.9%
5
 
0.1%
Space Separator
ValueCountFrequency (%)
608521
100.0%
Dash Punctuation
ValueCountFrequency (%)
-9155
100.0%
Close Punctuation
ValueCountFrequency (%)
)3473
100.0%
Lowercase Letter
ValueCountFrequency (%)
ì5
100.0%
Modifier Symbol
ValueCountFrequency (%)
`2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4363854
82.7%
Common910557
 
17.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A657181
15.1%
O399689
 
9.2%
N367918
 
8.4%
E355286
 
8.1%
L335055
 
7.7%
R244168
 
5.6%
S232267
 
5.3%
T223176
 
5.1%
I218341
 
5.0%
C209821
 
4.8%
Other values (19)1120952
25.7%
Common
ValueCountFrequency (%)
608521
66.8%
,225888
 
24.8%
.48594
 
5.3%
-9155
 
1.0%
(3482
 
0.4%
)3473
 
0.4%
13387
 
0.4%
22318
 
0.3%
31483
 
0.2%
41027
 
0.1%
Other values (16)3229
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5271164
99.9%
None3242
 
0.1%
Punctuation5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A657181
12.5%
608521
 
11.5%
O399689
 
7.6%
N367918
 
7.0%
E355286
 
6.7%
L335055
 
6.4%
R244168
 
4.6%
S232267
 
4.4%
,225888
 
4.3%
T223176
 
4.2%
Other values (41)1622015
30.8%
None
ValueCountFrequency (%)
Ñ3232
99.7%
Ä5
 
0.2%
ì5
 
0.2%
Punctuation
ValueCountFrequency (%)
5
100.0%

Interactions

2022-05-22T00:39:33.728827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:11.400960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:13.175961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:14.970375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:16.723951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:18.935871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:20.761857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:22.482927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:24.312111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:26.098932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:27.875328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:29.671736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:31.943133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:33.860515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:11.523677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:13.303366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:15.097430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:16.860630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:19.067169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:20.888200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:22.611865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:24.450023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:26.228948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:28.004729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:30.225953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:32.073477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:34.037688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:11.651290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:13.432693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:15.229749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:17.007982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:19.205797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:21.016732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:22.745298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:24.580959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:26.361586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:28.138627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:30.366513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:32.205345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:34.224602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:11.787774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:13.568120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:15.361946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:17.145258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:19.345132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:21.147526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:22.884910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:24.724927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:26.496618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:28.277504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:30.507272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:32.341460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:34.431094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:11.927914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:13.708806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:15.497115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:17.285178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:19.487445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:21.284479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:23.024009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:24.866055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:26.632788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:28.426084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:30.651336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:32.486324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:34.620661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:12.064068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:13.852593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:15.634931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:17.432232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:19.628523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:21.419535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:23.164633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:25.010358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:26.775722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:28.569055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:30.796487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:32.626657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:34.789423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:12.191149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:13.979659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:15.762573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:17.564572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:19.760114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:21.548421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:23.288751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:25.137503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:26.909189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:28.701014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:30.930402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:32.754281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:34.977616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:12.323980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:14.117912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:15.898968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:17.705657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:19.901103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:21.684058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:23.481382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:25.277704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:27.044775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:28.838295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:31.075822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:32.894736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:35.161358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:12.449324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:14.253179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:16.032226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:17.845459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:20.034593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:21.814234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:23.609792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:25.407690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:27.176950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:28.971854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:31.210701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:33.030413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:35.351384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:12.584048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:14.392063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:16.166534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:17.986890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:20.177868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:21.951825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:23.755444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:25.548658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:27.312643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:29.110367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:31.353901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:33.169183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:35.535790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:12.722021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:14.549680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:16.305122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:18.125289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:20.316731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:22.083318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:23.894447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:25.682963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:27.454835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:29.246101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:31.515800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:33.304606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:35.724450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:12.861747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:14.691969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:16.443469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:18.266767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:20.467631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:22.217509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:24.034032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:25.823276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:27.593574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:29.389153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:31.659262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:33.447228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:35.905778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:13.041020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:14.832410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:16.578732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:18.781176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:20.608793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:22.350692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:24.168336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:25.961743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:27.733796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:29.526894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:31.799848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-22T00:39:33.584556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-22T00:39:41.807422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-22T00:39:42.019052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-22T00:39:42.237079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-22T00:39:42.494213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-22T00:39:42.643696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-22T00:39:36.304016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-22T00:39:36.910863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

regionprovincecitybarangayprecinct_idABELLA, ERNIE (IND)DE GUZMAN, LEODY (PLM)DOMAGOSO, ISKO MORENO (AKSYON)GONZALES, NORBERTO (PDSP)LACSON, PING (PDR)MANGONDATO, FAISAL (KTPNAN)MARCOS, BONGBONG (PFP)MONTEMAYOR, JOSE JR. (DPP)PACQUIAO, MANNY PACMAN(PROMDI)ROBREDO, LENI (IND)CLUSTERCLUSTERTOTALCLUSTERED_PRECINCTSPOLLINGCENTER
0REGION XBUKIDNONBAUNGONBALINTAD130100092140012240115299.0486.00014A, 0015A, 0015B, 0014BBALINTAD, ELEMENTARY SCHOOL, BALINTAD, BAUNGON, BUKIDNON
1REGION XBUKIDNONBAUNGONBUENAVISTA1301001035750231011884710.0769.00016A, 0016B, 0017A, 0017B, 0017CBUENAVISTA, ELEMENTARY SCHOOL, BUENAVISTA, BAUNGON, BUKIDNON
2REGION XBUKIDNONBAUNGONDANATAG1301001143943028841524411.0645.00018A, 0018B, 0019A, 0019BDANATAG, ELEMENTARY SCHOOL, DANATAG, BAUNGON, BUKIDNON
3REGION XBUKIDNONBAUNGONDANATAG13010012221623019031605112.0592.00020A, 0020B, 0021A, 0021B, 0024ADANATAG, ELEMENTARY SCHOOL, DANATAG, BAUNGON, BUKIDNON
4REGION XBUKIDNONBAUNGONDANATAG13010013011646027221254613.0593.00022A, 0022B, 0023A, 0023BDANATAG, ELEMENTARY SCHOOL, DANATAG, BAUNGON, BUKIDNON
5REGION XBUKIDNONBAUNGONKALILANGAN13010014601013132001405014.0712.00025A, 0026A, 0026B, 0027A, 0027BKALILANGAN, ELEMENTARY SCHOOL, KALILANGAN, BAUNGON, BUKIDNON
6REGION XBUKIDNONBAUNGONLACOLAC1301001531515127511516215.0698.00028A, 0028B, 0029A, 0029B, 0030ALACOLAC ELEMENTARY SCHOOL, LACOLAC, BAUNGON, BUKIDNON
7REGION XBUKIDNONBAUNGONLANGAON1301001610400115711464116.0429.00031A, 0031B, 0031CLANGAON ELEMENTARY SCHOOL, LANGAON, BAUNGON, BUKIDNON
8REGION XBUKIDNONBAUNGONLANGAON1301001712313117411313617.0456.00032A, 0032B, 0033A, 0033BLANGAON ELEMENTARY SCHOOL, LANGAON, BAUNGON, BUKIDNON
9REGION XBUKIDNONBAUNGONLIBORAN1301001854624026612355118.0706.00034A, 0034B, 0035A, 0035BLIBORAN ELEMENTARY SCHOOL, LIBORAN, BAUNGON, BUKIDNON

Last rows

regionprovincecitybarangayprecinct_idABELLA, ERNIE (IND)DE GUZMAN, LEODY (PLM)DOMAGOSO, ISKO MORENO (AKSYON)GONZALES, NORBERTO (PDSP)LACSON, PING (PDR)MANGONDATO, FAISAL (KTPNAN)MARCOS, BONGBONG (PFP)MONTEMAYOR, JOSE JR. (DPP)PACQUIAO, MANNY PACMAN(PROMDI)ROBREDO, LENI (IND)CLUSTERCLUSTERTOTALCLUSTERED_PRECINCTSPOLLINGCENTER
90934REGION IIIBATAANSAMALTABING ILOG8120009004617036701999.0592.00018B, 0019A, 0019B, 0020A, 0020BSAMAL NORTH ELEMENTARY SCHOOL, SAN JUAN
90935REGION IIIBATAANSAMALGUGO81200143125021036601110314.0608.00030A, 0030B, 0031A, 0031CGUGO ELEMENTARY SCHOOL, GUGO
90936REGION IIIBATAANSAMALGUGO812001541500804330118715.0686.00030C, 0032A, 0032B, 0032CGUGO ELEMENTARY SCHOOL, GUGO
90937REGION IIIBATAANSAMALGUGO812001655470914130128316.0658.00031B, 0033A, 0033B, 0033CGUGO ELEMENTARY SCHOOL, GUGO
90938REGION IIIBATAANSAMALGUGO8120017114416036401412417.0626.00034A, 0034B, 0034C, 0035DGUGO ELEMENTARY SCHOOL, GUGO
90939REGION IIIBATAANSAMALGUGO8120018303807034711212418.0586.00035A, 0035B, 0035CGUGO ELEMENTARY SCHOOL, GUGO
90940REGION IIIBATAANSAMALWEST CALAGUIMAN (POB.)812004920582512430719749.0581.00088A, 0088B, 0089A, 0089BCALAGUIMAN ELEMENTARY SCHOOL, EAST CALAGUIMAN
90941REGION IIIBATAANSAMALWEST CALAGUIMAN (POB.)8120050003711002331523650.0575.00090A, 0090B, 0091A, 0091BCALAGUIMAN ELEMENTARY SCHOOL, EAST CALAGUIMAN
90942REGION IIIBATAANSAMALWEST DAANG BAGO (POB.)812000331202110194091273.0412.00006A, 0009A, 0009BSAMAL NORTH ELEMENTARY SCHOOL, SAN JUAN
90943REGION IIIBATAANSAMALWEST DAANG BAGO (POB.)812000410262120215021384.0441.00006B, 0007A, 0008ASAMAL NORTH ELEMENTARY SCHOOL, SAN JUAN